new requirements

[rathoddinesh14]

Here’s a list of GitHub issues for the Real-Time Healthcare Monitoring and Alert System with Apache Kafka project. Each issue includes a clear title, description, and sometimes additional details like priority or task breakdown.

GitHub Issues:

1. Set Up Apache Kafka Cluster

   • Description: Set up a multi-node Kafka cluster to handle real-time data streams from medical devices.
   • Tasks:
     • Configure Kafka broker(s)
     • Set up Zookeeper for managing Kafka nodes
     • Create sample Kafka topics for initial testing (heart_rate, bp, etc.)

2. Implement Kafka Producer for Medical Device Data

   • Description: Develop a Kafka producer that pushes real-time data (e.g., heart rate, BP) from IoT medical devices to Kafka topics.
   • Tasks:
     • Integrate with IoT gateway
     • Serialize medical data in JSON format
     • Push data to relevant Kafka topics

3. Design Kafka Consumer for Real-Time Analytics

   • Description: Create a Kafka consumer to read data from Kafka topics and perform real-time analytics on patient vitals.
   • Tasks:
     • Read from multiple Kafka topics (e.g., heart_rate, bp)
     • Define alert rules (e.g., heart rate > 120 bpm)
     • Log analytics and possible alerts

4. Integrate Kafka Streams for Anomaly Detection

   • Description: Implement Kafka Streams to process real-time data and detect anomalies in patient vitals.
   • Tasks:
     • Set thresholds for vitals (heart rate, blood pressure, oxygen level)
     • Implement stream processing logic to detect violations
     • Send processed results to alert system

5. Develop Real-Time Alert System

   • Description: Create a notification system that sends alerts to healthcare professionals when abnormal patient vitals are detected.
   • Tasks:
     • Connect to Kafka Streams results
     • Integrate SMS/email service (Twilio, Firebase)
     • Define alert message templates

6. Implement REST API for Device Registration

   • Description: Build a REST API to register new medical devices and patients into the system.
   • Tasks:
     • Implement device registration (device ID, patient details)
     • Store information in the backend database
     • Validate device and patient data

7. Create Patient Monitoring Dashboard UI

   • Description: Develop a frontend dashboard for healthcare professionals to monitor real-time patient data.
   • Tasks:
     • Display real-time vitals from Kafka consumers
     • Show historical trends for individual patients
     • Integrate alert notifications

8. Store Data Using Kafka Connect and MongoDB/PostgreSQL

   • Description: Set up Kafka Connect to transfer real-time Kafka topic data to MongoDB or PostgreSQL for long-term storage.
   • Tasks:
     • Configure Kafka Connect with MongoDB sink
     • Map Kafka topic data schema to database schema
     • Ensure fault tolerance and error handling

9. Implement Historical Data Analytics

   • Description: Enable retrospective analysis of stored patient data using historical analytics.
   • Tasks:
     • Query stored data from MongoDB/PostgreSQL
     • Perform analytics such as average heart rate over time
     • Visualize historical trends in patient dashboard

10. Add Security Layer with OAuth and SSL

    • Description: Secure the communication between devices, Kafka, and the backend using OAuth for authentication and SSL for data encryption.
    • Tasks:
      • Implement OAuth for user authentication
      • Configure SSL/TLS encryption for Kafka communication
      • Set role-based access controls for sensitive data

11. Unit Test Kafka Producer and Consumer

    • Description: Write unit tests for Kafka producers and consumers to ensure that real-time data is processed correctly.
    • Tasks:
      • Mock Kafka brokers for testing
      • Test data serialization and deserialization
      • Verify correct message flow between producer and consumer

12. Integrate Machine Learning Models for Predictive Alerts

    • Description: Train and integrate machine learning models for predicting potential health risks based on patient vitals.
    • Tasks:
      • Use historical patient data to train models
      • Integrate model into real-time stream for predictions
      • Trigger alerts based on model output

13. Handle Kafka Topic Partitioning and Replication

    • Description: Ensure Kafka topics are partitioned and replicated properly to handle scaling and failover scenarios.
    • Tasks:
      • Configure topic partitioning based on expected data volume
      • Set replication factor to ensure data durability
      • Test for partition rebalancing and fault tolerance

14. Implement Role-Based Access Control (RBAC)

    • Description: Implement role-based access control to limit access to sensitive patient data.
    • Tasks:
      • Define user roles (Admin, Doctor, Nurse)
      • Implement role-specific access to dashboards and data
      • Secure the API endpoints based on user roles

15. Create Documentation for Setting up and Running the System

    • Description: Write detailed documentation for setting up the Kafka cluster, producing/consuming data, and using the monitoring dashboard.
    • Tasks:
      • Setup instructions for Kafka and Kafka Connect
      • Instructions for running the producer and consumer
      • User guide for the patient monitoring dashboard

16. Optimize Kafka Performance for Large Data Volume

    • Description: Tune Kafka performance settings to handle a large volume of real-time medical data.
    • Tasks:
      • Adjust broker memory, message size, and topic configurations
      • Monitor Kafka metrics (e.g., throughput, latency)
      • Optimize data serialization/deserialization for better performance

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